Aggregating with count(), summarize(), group_by(), case_when()

Author

Emily Malcolm-White

#LOAD PACKAGES 
library(tidyverse)

`palmerpenguins` dataset

Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program.

#LOAD DATA 
library(palmerpenguins)
data(penguins)

1: Load the palmerpenguins package
2: Display the penguins dataset in the environment

Remove rows with missing data with `drop_na()`

penguins <- penguins %>%
  drop_na()

1: Drops all the rows in the penguins dataset which has missing data (NA values)
2: overwrite the penguins dataset with the penguins dataset without the missing rows

Warning

Is it appropriate to remove rows with missing data? How many rows have missing data? Do the missing rows have something in common?

Removing rows can affect the validity and generalizability of your analysis!

`count`

count() lets you quickly count the unique values of one or more variables. Suppose you want the number of penguins on each island.

penguins %>% 
    count(island)

# A tibble: 3 × 2
  island        n
  <fct>     <int>
1 Biscoe      163
2 Dream       123
3 Torgersen    47

`summarize` or `summarise` (either works)

Suppose we are interested in the average bill length of all Adelie penguins:

penguins %>%                                            
  filter(species == "Adelie") %>%
  summarize(average_bill_length = mean(bill_length_mm))

1: only include the rows where the species is Adelie
2: calculate the average bill length; save this as average_bill_length

# A tibble: 1 × 1
  average_bill_length
                <dbl>
1                38.8

Suppose we are interested in the average bill length AND average bill depth of all Adelie penguins:

penguins %>%
  filter(species == "Adelie") %>%
  summarize(average_bill_lenth = mean(bill_length_mm),
            average_bill_depth = mean(bill_depth_mm))

1: calculate the average bill length; save this as average_bill_length
2: calculate the average bill depth; save this as average_bill_depth

# A tibble: 1 × 2
  average_bill_lenth average_bill_depth
               <dbl>              <dbl>
1               38.8               18.3

Typically, we seperate each calculation with a new line to keep things pretty. These new values will print out on the same table.

There are lots of other functions available:

min: minimum value
max: maximum value
mean: average or mean value
median: median value
var: variance
sd: standard deviation
n: count or number of values
n_distinct: counts number of distinct values

Suppose we are interested in the average bill length AND the median bill length of all Adelie penguins:

penguins %>%
  filter(species == "Adelie") %>%
  summarise(average_bill_lenth = mean(bill_length_mm), 
            median_bill_length = median(bill_length_mm))

# A tibble: 1 × 2
  average_bill_lenth median_bill_length
               <dbl>              <dbl>
1               38.8               38.8

`group_by`

Let’s say we were interested in the average bill length and bill depth of all penguin species in this dataset. We could repeat this for the other species (Gentoo and Chinstrap). This would be a fair amount of work AND the results would not end up in the same table.

OR we could use the group_by command!

penguins %>%
  group_by(species) %>%
  summarise(average_bill_lenth = mean(bill_length_mm), 
            average_bill_depth = mean(bill_depth_mm))

1: Repeats the calculate below for each different species.

# A tibble: 3 × 3
  species   average_bill_lenth average_bill_depth
  <fct>                  <dbl>              <dbl>
1 Adelie                  38.8               18.3
2 Chinstrap               48.8               18.4
3 Gentoo                  47.6               15.0

Multiple Groups

Suppose we wish to have the average bill length and average bill depth broken down by sex AND species:

penguins %>%
  group_by(species, sex) %>%
  summarise(average_bill_length = mean(bill_length_mm), 
            average_bill_depth = mean(bill_depth_mm))

# A tibble: 6 × 4
# Groups:   species [3]
  species   sex    average_bill_length average_bill_depth
  <fct>     <fct>                <dbl>              <dbl>
1 Adelie    female                37.3               17.6
2 Adelie    male                  40.4               19.1
3 Chinstrap female                46.6               17.6
4 Chinstrap male                  51.1               19.3
5 Gentoo    female                45.6               14.2
6 Gentoo    male                  49.5               15.7

(Optional) `across`

If you wish to apply the same calculation to many columns, you may wish to check out the across function. Artwork by @allisonhorst

Recall: `mutate`

The mutate function allows you create a new column which is a function of other columns. This can be useful to converting units.

For example, let’s calculate create a new column which displays the body length weight in pounds (lbs) instead of grams. Recall: to convert from grams to pounds we need to multiply by 0.00220462

penguins <- penguins %>% 
  mutate(body_mass_lbs = body_mass_g*0.00220462)

1: Creates a new column in the penguins dataset called body_mass_lbs calculated by taking the value of the body mass (in g) and multiplying by 0.00220562.

`case_when`

Case when can be used in combination with mutate to create a new column with a categorical variable conditional on the values in another column.

For example:

penguins <- penguins %>% 
  mutate(penguin_length_cat = case_when(bill_length_mm > 50 ~ 'whoa! huge bill!', TRUE ~ '--' ))

Tip

For those of you who have taken a computer science class before, you may notice that case_when is similar to using an ifelse statement. You can also use ifelse in R if you’d prefer!

penguins <- penguins %>% 
  mutate(penguin_length_cat = ifelse(bill_length_mm > 50, 'whoa! huge bill!', '--' ))

Brain Break

This is a story about Jinjing the South American Magellanic Penguin, that swims 5,000 miles each year to be reunited with the man who saved his life. The rescued Penguin was saved by João Pereira de Souza, a 73 year old part-time fisherman, who lives in an island village just outside Rio de Janeiro, Brazil. https://youtu.be/oks2R4LqWtE

--- title: 'Aggregating with count(), summarize(), group_by(), case_when()' author: 'Emily Malcolm-White' format: html: toc: TRUE code-overflow: wrap embed-resources: true code-tools: source: true toggle: false caption: none code-annotations: hover execute: message: FALSE warning: FALSE --- ```{r message=FALSE, warning=FALSE} #LOAD PACKAGES library(tidyverse) ``` # `palmerpenguins` dataset Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program. ![](https://allisonhorst.github.io/palmerpenguins/logo.png){width=30%} ```{r} #LOAD DATA library(palmerpenguins) #<1> data(penguins) #<2> ``` 1. Load the `palmerpenguins` package 2. Display the penguins dataset in the environment # Remove rows with missing data with `drop_na()` ```{r} penguins <- penguins %>% #<2> drop_na() #<1> ``` 1. Drops all the rows in the penguins dataset which has missing data (NA values) 2. overwrite the penguins dataset with the penguins dataset without the missing rows ::: callout-warning Is it appropriate to remove rows with missing data? How many rows have missing data? Do the missing rows have something in common? Removing rows can affect the validity and generalizability of your analysis! ::: # `count` `count()` lets you quickly count the unique values of one or more variables. Suppose you want the number of penguins on each island. ```{r} penguins %>% count(island) ``` # `summarize` or `summarise` (either works) Suppose we are interested in the average bill length of all Adelie penguins: ```{r} penguins %>% filter(species == "Adelie") %>% #<1> summarize(average_bill_length = mean(bill_length_mm)) #<2> ``` 1. only include the rows where the species is Adelie 2. calculate the average bill length; save this as `average_bill_length` Suppose we are interested in the average bill length AND average bill depth of all Adelie penguins: ```{r} penguins %>% filter(species == "Adelie") %>% summarize(average_bill_lenth = mean(bill_length_mm), #<1> average_bill_depth = mean(bill_depth_mm)) #<2> ``` 1. calculate the average bill length; save this as `average_bill_length` 2. calculate the average bill depth; save this as `average_bill_depth` Typically, we seperate each calculation with a new line to keep things pretty. These new values will print out on the same table. There are lots of other functions available: - `min`: minimum value - `max`: maximum value - `mean`: average or mean value - `median`: median value - `var`: variance - `sd`: standard deviation - `n`: count or number of values - `n_distinct`: counts number of distinct values Suppose we are interested in the average bill length AND the median bill length of all Adelie penguins: ```{r} penguins %>% filter(species == "Adelie") %>% summarise(average_bill_lenth = mean(bill_length_mm), median_bill_length = median(bill_length_mm)) ``` # `group_by` Let's say we were interested in the average bill length and bill depth of all penguin species in this dataset. We could repeat this for the other species (Gentoo and Chinstrap). This would be a fair amount of work AND the results would not end up in the same table. OR we could use the `group_by` command! ```{r} penguins %>% group_by(species) %>% #<1> summarise(average_bill_lenth = mean(bill_length_mm), average_bill_depth = mean(bill_depth_mm)) ``` 1. Repeats the calculate below for each different species. ## Multiple Groups Suppose we wish to have the average bill length and average bill depth broken down by sex AND species: ```{r} penguins %>% group_by(species, sex) %>% summarise(average_bill_length = mean(bill_length_mm), average_bill_depth = mean(bill_depth_mm)) ``` # (Optional) `across` If you wish to apply the same calculation to many columns, you may wish to check out the `across` function. ![Artwork by @allisonhorst](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/2471e3f8-348e-470c-a162-3eea0606ff96_rw_1920.png?h=1caf8d5f3d25792fbaf296a1e62b670a) # Recall: `mutate` The mutate function allows you create a new column which is a function of other columns. This can be useful to converting units. For example, let's calculate create a new column which displays the body length weight in pounds (lbs) instead of grams. Recall: to convert from grams to pounds we need to multiply by 0.00220462 ```{r} penguins <- penguins %>% mutate(body_mass_lbs = body_mass_g*0.00220462) #<1> ``` 1. Creates a new column in the penguins dataset called `body_mass_lbs` calculated by taking the value of the body mass (in g) and multiplying by 0.00220562. # `case_when` Case when can be used in combination with `mutate` to create a new column with a categorical variable conditional on the values in another column. ![Artwork by @allisonhorst](https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/6ffcd6d6-c783-4087-ae76-99aa851663ed_rw_1920.png?h=5f67935d69c92e1fda9074d00dfea972){width=50%} For example: ```{r} penguins <- penguins %>% mutate(penguin_length_cat = case_when(bill_length_mm > 50 ~ 'whoa! huge bill!', TRUE ~ '--' )) ``` ::: callout-tip For those of you who have taken a computer science class before, you may notice that `case_when` is similar to using an `ifelse` statement. You can also use `ifelse` in R if you'd prefer! ```{r} penguins <- penguins %>% mutate(penguin_length_cat = ifelse(bill_length_mm > 50, 'whoa! huge bill!', '--' )) ``` ::: # Brain Break This is a story about Jinjing the South American Magellanic Penguin, that swims 5,000 miles each year to be reunited with the man who saved his life. The rescued Penguin was saved by João Pereira de Souza, a 73 year old part-time fisherman, who lives in an island village just outside Rio de Janeiro, Brazil. <https://youtu.be/oks2R4LqWtE> <iframe width="560" height="315" src="https://www.youtube.com/embed/oks2R4LqWtE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen> </iframe>

palmerpenguins dataset

Remove rows with missing data with drop_na()

count

summarize or summarise (either works)

group_by

Multiple Groups

(Optional) across

Recall: mutate

case_when

Brain Break

`palmerpenguins` dataset

Remove rows with missing data with `drop_na()`

`count`

`summarize` or `summarise` (either works)

`group_by`

(Optional) `across`

Recall: `mutate`

`case_when`